This project will use novel quantitative imaging methods to guide biopsies to biologically distinct regions of brain tumors for targeted exome and transcriptome analysis. Our goal is to identify naturally evolving and treatment-induced mutations that drive malignant transformation (MT) of low grade glioma (LGG) to high grade glioma (HGG). MT is associated with very poor survival, but the mechanisms underlying MT are unknown, and it is not known how chemotherapy following resection of LGG might alter the natural course of tumor evolution. Our substantial preliminary data from exome and RNA sequencing (RNA-seq) suggests that evolution of mutations can differ dramatically in temozolomide (TMZ) treated and non-treated patients, and that this commonly used chemotherapeutic agent itself may induce recurring transformation-promoting driver mutations that converge on common signaling pathways. In contrast to traditional genomic studies, imaging guided genomics could enrich the detection of mutations that drive MT by linking mutations to regions of aggressive tumor growth in vivo. Here we propose to interrogate the genetic underpinnings of MT in TMZ-treated and untreated patients with two complementary approaches. In aim 1, we will use exome and RNA-seq to compare exon mutations and expression profiles among four tumor biopsies from each patient, two with and two without characteristics of MT as predicted by novel physiologic/metabolic imaging parameters and subsequently confirmed by tissue analyses. This will provide a focused assessment of MT from a single surgical time point. In aim 2, we will use longitudinally collected samples from the same individual before and after transition from LGG to HGG. We will compare the mutation and expression profiles within this second set of subjects who have (i) LGG tissue available retrospectively and (ii) image guided tissue samples that were obtained as part of this grant and that demonstrate transformation to HGG. These paired samples will allow a direct assessment of evolution of mutations in individual patients over time. The integration of genomics with advanced imaging, validation of mutation frequency in large, independent set of tumors, experimental assays of candidates, and up-to-date computational analyses are expected to enrich for the identification of mutations that drive MT and to distinguish naturally evolving from TMZ-induced mutations. These studies could therefore impact patient management by identifying LGG patients for which chemotherapy should be contraindicated, and by identifying common and targetable mutations associated with MT.